The following co-pending US Patent Applications, filed on Jun. 6, 2011, may include related subject matter: Ser. No. 13/168,961, Ser. No. 13/168,964, Ser. No. 13/168,965, Ser. No. 13/168,967, Ser. No. 13/168,968, Ser. No. 13/168,971, Ser. No. 13/168,972, and Ser. No. 13/168,973.
The field of affective computing involves the study and development of systems and devices that can recognize, interpret, process, and even simulate human affects. This field has undergone dramatic developments in recent years. The continuing increase in computing power, coupled with the miniaturization and proliferation of sensors and mobile devices, has made the prospect of widespread adoption of affective computing systems in real world day-to-day scenarios an imminent reality.
Many existing affective computing systems are still only developed for research purposes. As such, the data collected and stored in databases for the systems' development is typically generated in a controlled environment. In these laboratory-like settings, a small number of short experiments (typically less than an hour long), is conducted in which a user's reactions are measured to a set of pre-selected stimuli such as pictures, video scenes, or music. While sufficient for laboratory-based affective computing systems, this data is insufficient for creating the strong and personalized affective computing-based applications that are to come in the next few years. One drawback of using laboratory-collected data is that the data is acquired over a short period of time, while the user is in a specific, controlled situation. In reality, a user's reaction to stimuli can vary dramatically depending on the situation the user is in. For example, a user's affective response while working in the office be quite different from the user's response when relaxing at home, even if exposed to the very similar stimuli in both situations. Furthermore, in short experiments, a user's response can only be measured to a small number of stimuli, which may prove to be inadequate for creating an affective computing system for real world applications that may have to model the effect of a wide range of stimuli from multiple sources (such as images, sounds, words, flavors, scents, and physical sensations).
Analyzing affective response data collected in real world scenarios poses new challenges, which do not arise with data that is collected in controlled laboratory-like situations. For example, in the laboratory the user's response is usually measured for a single stimulus at a time, which is not like the real world, where the user is simultaneously exposed to many stimuli of different natures and possibly originating from various sources. Moreover, in the laboratory the user is not distracted by various real world stimuli, such as other people in the user's vicinity, external noises, or even simply being pre-occupied or daydreaming. Therefore, there is a need for a new data structure that will reflect these real world complications, and enable the affective computing systems using the data to accurately determine the user's affective response to various stimuli.